ArticleSensors (Basel, Switzerland)2018
ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.
- Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Deep learning-based gait phase detection using shank-mounted IMU data: Classification approach.PloS one · 2026Article
- Real-Time Balancing of Stability and Plasticity in Continual Learning Enables Adaptive Speed Estimation for Lower-Limb Prostheses.IEEE transactions on medical robotics and bionics · 2025Article
- Tibial Artery Stent Deformations From Ankle Plantarflexion and Dorsiflexion, Knee Flexion, and Simulated Calf Muscle Contraction.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Article
- Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects.Sensors (Basel, Switzerland) · 2024Article
- Ground Contact Force and Moment Estimation for Human-Exoskeleton Systems Using Dynamic Decoupled Coordinate System and Minimum Energy Hypothesis.Biomimetics (Basel, Switzerland) · 2023Article
- Enhancing Wearable Gait Monitoring Systems: Identifying Optimal Kinematic Inputs in Typical Adolescents.Sensors (Basel, Switzerland) · 2023Article
- A Review of Current State-of-the-Art Control Methods for Lower-Limb Powered Prostheses.Annual reviews in control · 2023Article
- Carrying Position-Independent Ensemble Machine Learning Step-Counting Algorithm for Smartphones.Sensors (Basel, Switzerland) · 2022Article
- Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis.IEEE transactions on bio-medical engineering · 2022Article
- Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion.Applied bionics and biomechanics · 2022Review
- Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection.Frontiers in sports and active living · 2022Article
- Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit.Frontiers in neurorobotics · 2022Article
- Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons.Sensors (Basel, Switzerland) · 2021Article
- The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson's Patients.Sensors (Basel, Switzerland) · 2021Article
- A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons.Frontiers in neurorobotics · 2021Article
- Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation.Sensors (Basel, Switzerland) · 2020Article
- An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection.International journal of environmental research and public health · 2020Article
- A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses.Sensors (Basel, Switzerland) · 2020Review
- Walking with a powered ankle-foot orthosis: the effects of actuation timing and stiffness level on healthy users.Journal of neuroengineering and rehabilitation · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Throughout the last decade, a whole new generation of powered transtibial prostheses and exoskeletons has been developed. However, these technologies are limited by a gait phase detection which controls the wearable device as a function of the activities of the wearer. Consequently, gait phase detection is considered to be of great importance, as achieving high detection accuracy will produce a more precise, stable, and safe rehabilitation device. In this paper, we propose a novel gait percent detection algorithm that can predict a full gait cycle discretised within a 1% interval. We called this algorithm an exponentially delayed fully connected neural network (ED-FNN). A dataset was obtained from seven healthy subjects that performed daily walking activities on the flat ground and a 15-degree slope. The signals were taken from only one inertial measurement unit (IMU) attached to the lower shank. The dataset was divided into training and validation datasets for every subject, and the mean square error (MSE) error between the model prediction and the real percentage of the gait was computed. An average MSE of 0.00522 was obtained for every subject in both training and validation sets, and an average MSE of 0.006 for the training set and 0.0116 for the validation set was obtained when combining all subjects' signals together. Although our experiments were conducted in an offline setting, due to the forecasting capabilities of the ED-FNN, our system provides an opportunity to eliminate detection delays for real-time applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.